Lecture 6 - Other Distributed Systems - PowerPoint PPT Presentation

Lecture 6 - Other Distributed Systems

CSE 490h – Introduction to Distributed Computing, Spring 2007

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• DNS
• BOINC
• PlanetLab
• OLPC & Ad-hoc Mesh Networks
• Lecture content wrap-up

DNS: The Distributed System in the Distributed System

• Mnemonic identifiers work considerably better for humans than IP addresses

“www.google.com? Surely you mean 66.102.7.99!”

• Who maintains the mappings from nameIP?

© 2006 Computer History Museum. All rights reserved.

www.computerhistory.org

• Every machine had a file named hosts.txt
• Each line contained a name/IP mapping
• New hosts files were updated and distributed via email

… This clearly wasn’t going to scale

• Modern DNS system first proposed in 1983
• First implementation in 1984 (Paul Mockapetris)
• BIND (Berkeley Internet Name Domain) written by four Berkeley students in 1985.
• Many other implementations today

• DNS names are arranged in a hierarchy:

www.cs.washington.edu

• Entries are either subdomains or hostnames
• subdomains contain more subdomains, or hosts (up to 127 levels deep!)
• Hosts have individual IP addresses

• DNS Recurser (client) parses address from right to left
• Asks root server (with known, static IP address) for name of first subdomain DNS server
• Contacts successive DNS servers until it finds the host

• ISPs provide a DNS recurser for clients
• DNS recursers cache lookups for period of time after a request
• Greatly speeds up retrieval of entries and reduces system load

BOINC

• “Berkeley Open Infrastructure for Network Computing”
• Platform for Internet-wide distributed applications
• Volunteer computing infrastructure
• Relies on many far-flung users volunteering spare CPU power

• 1,000,000+ active nodes
• 521 TFLOPS of computing power
• 20 active projects (SETI@Home, Folding@Home, Malaria Control…) and several more in development

(Current as of March 2007)

• Both are frameworks on which “useful” systems can be built
• Does not prescribe particular programming style
• Much more heterogeneous architecture
• Does not have a formal aggregation step
• Designed for much longer-running systems (months/years vs. minutes/hours)

• Central server runs LAMP architecture for web + database
• End-users run client application with modules for actual computation
• BitTorrent used to distribute data elements efficiently

• Homogenous redundancy
• Work unit “trickling”
• Locality scheduling
• Distribution based on host parameters

• Available as regular application, background “service”, or screensaver
• Can be administered locally or LAN-administered via RPC
• Can be configured to use only “low priority” cycles

• Client software runs on variety of operating systems, each with different IPC
• Uses shared memory message passing to transmit information from “manager” to actual tasks and vice versa

• Sense of accomplishment, community involvement, or scientific duty
• Stress testing machines/networks
• Potential for fame (if your computer “finds” an alien planet, you can name it!)
• “Bragging rights” for computing more units
• “BOINC Credits”

• Work done is rewarded with “cobblestones”
• 100 cobblestones = 1 day of CPU time for a computer with performance equaling 1,000 double-precision floating-point MIPS (Whetstone) & 1,000 integer VAX MIPS (Dhrystone)
• Computers are benchmarked by the BOINC system and receive credit appropriate to their machine

• Work units are computed redundantly by several different machines, and results are compared by the central server for consistency
• Credit is awarded after the internal server validates the returned work units
• Work units must be returned before a deadline

• Versatile infrastructure
• SETI tasks take a few hours
• Climate simulation tasks take months
• Network monitoring tasks are not CPU-bound at all!
• Scales extremely well to internet-wide applications
• Provides another flexible middleware layer to base distributed applications on
• Volunteer computing comes with add’l considerations (rewards, cheating)

PlanetLab

• Test a new version of Bittorrent that might generate GB’s and GB’s of data?
• Design a new distributed hashtable algorithm for thousands of nodes?
• Create a gigantic caching structure that mirrored web pages in several sites across the USA?

• Each of these problems requires:
• Hundreds or thousands of servers
• Geographic distribution
• An isolated network for testing and controlled experiments
• Developing one-off systems to support these would be
• Costly
• Redundant

• A multi-university effort to build a network for large-scale simulation, testing, and research
• “Simulate the Internet”

• Servers: 722+
• Slices: 600+
• Users: 2500+
• Bytes-per-day: 3 - 4 TB
• IP-flows-per-day: 190M
• Unique IP-addrs-per-day: 1M

As of Fall, 2006

• Supports short- and long-term research goals
• System put up “as fast as possible” – PlanetLab design evolves over time to meet changing needs
• PlanetLab is a process, not a result

Projects must be isolated from one another

• Code from several researchers:
• Untrustworthy? Possibly buggy?
• Intellectual property issues?
• Time-sensitive experiments must not interfere with one another
• Must provide realistic workload simulations

• Built on Linux, ssh, other standard tools
• Provides “normal” environment for application development
• Hosted at multiple universities w/ separate admins
• Requires trust relationships with respect to previous goals

• Network is divided into “slices” – server pools created out of virtual machines
• Trusted intermediary “PLC” system grants access to network resources
• Allows universities to specify who can use slices at each site
• Distributed trust relationships
• Central system control  Federated control

• PLC authenticates users and understands relationships between principals; issues tickets
• SHARP system at site validates ticket + returns lease

• Public-key cryptography used to sign modules entered into PlanetLab
• X.509 + SSL keys are used by PLC + slices to verify user authenticity
• Keys distributed “out of band” ahead of time

• Large system with complex relationships
• Currently upgrading to version 4.0
• New systems (GENI) are being proposed
• Still provides lots of resources to researchers
• CoralCache, several other projects run on PlanetLab

OLPC

“They want to deliver vast amounts of information over the Internet. And again, the Internet is not something you just dump something on. It's not a big truck. It's a series of tubes.”

• The internet is composed of a lot of infrastructure:
• Clients and servers
• Routers and switches
• Fiber optic trunk lines, telephone lines, tubes and trucks
• And if we map the density of this infrastructure…

… it probably looks something like this

Photo: cmu.edu

• What if we wanted to share a book?
• Pass it along, door-to-door.
• What if we wanted to share 10,000 books?
• Build community library.
• How about 10 million books? Or 300 copies of one book?
• A very large library?

• We need to build infrastructure to make large-scale distribution easy (i.e., computers and networking equipment)
• We need to be cheap
• Most of those dark spots don’t have much money
• We need reliability where reliable power is costly
• Again, did you notice that there weren’t so many lights? It’s because there’s no electricity!

• India
• 75% of people in rural villages
• 90% of phones in urban areas
• Many villagers share a single phone, usually located in the town post office
• Likewise, villages typically share a few computers, located at the school (or somewhere with reliable power)
• What’s the downside to this model?
• It might provide shared access to a lot of information, but it doesn’t solve the “300 copies of a book” case

• OLPC = One Laptop Per Child.

AKA: Children’s Machine, OLPC, $100 laptop

A cheap (~$150) laptop designed for children in developing countries

Photo: laptop.org

• Low power consumption
• No moving parts (flash memory, passive cooling)
• Dual-mode display
• In color, the XO consumes 2-3 watts
• In high-contrast monochrome, less than 1 watt
• Can be human powered by a foot-pedal
• Rugged, child-friendly design
• Low material costs
• Open-source software

• The XO utilizes far-reaching, low-power wireless networking to create ad-hoc mesh networks
• If any single XO is connected to the Internet, other nearby computers can share the connection in a peer-to-peer scheme
• Networks can theoretically sprawl as far as ten miles, even connecting nearby villages

• XO relies on network for content and collaboration
• Content is stored on a central servers
• Textbooks
• Cached websites (Wikipedia)
• User content
• Software makes it easy to see other users on the network and share content

XO must be purchased in orders of 1 million units by governments in developing nations (economies of scale help to lower costs)

Governments are responsible for distribution of laptops

Laptops are only for children, designed solely as a tool for learning

• Distribution downfalls
• What about children in developed nations?
• Sell to developed markets at a higher price to subsidize costs for developing nations.
• Can governments effectively distribute? What about black markets?
• OLPC could perhaps partner with local schools and other NGOs to aid in distribution, training and maintenance
• Too expensive?
• Some nations can only afford as much $20 per child per year. How can we cater to them?

• Today, only 16 percent of the world’s population is estimated to have access to the Internet
• Develop new markets
• Microcredit
• Make small loans to the impoverished without requiring collateral
• Muhammad Yunus and the Grameen Bank won the 2006 Nobel Peace Prize for their work here
• The power of the village economy
• As millions of users come online in developing nations, there will be many new opportunities for commerce.
• Helps those in developing nations to advance their economies and develop stronger economic models

• UN Millennium Development Goal #2: “achieve universal primary education”
• Empower children to think and compete in a global space
• Children are a nations greatest resource
• Backed by a bolstered economy, they will grow to solve other issues (infrastructure, poverty, famine)

• So what did we see in this class?
• Moore’s law is starting to fail
• More computing power means more machines
• This means breaking problems into sub problems
• Sub-problems cannot interfere with or depend on one another
• Have to “play nice” with shared memory

• MapReduce is one paradigm for breaking problems up
• Makes the “playing nice” easy by enforcing a decoupled programming model
• Handles lots of the behind-the-scenes work

• The network is a fundamental part of a distributed system
• Have to plan for bandwidth, latency, etc
• We’d like to think of the network as an abstraction
• Sockets = pipes
• RPC looks like a normal procedure call, handles tricky stuff under the hood
• Still have to plan for failures of all kinds

• The network allows us to make data available across many machines
• Network file systems can hook into existing infrastructure
• Specialized file systems (like GFS) can offer better performance with loss of generality
• Raises issues of concurrency, process isolation, and how to combat stale data

• There are lots of distributed systems out there
• MapReduce, BOINC, MPI, several other architectures, styles, problems to solve
• You might be designing an important one soon yourself!